summeryingliu/MIRL: Multiple Imputation Random Lasso for Variable Selection with Missing Entries

Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper: Liu Y, Wang Y, Feng Y, Wall MM. VARIABLE SELECTION AND PREDICTION WITH INCOMPLETE HIGH-DIMENSIONAL DATA. The annals of applied statistics. 2016;10(1):418-450.

Getting started

Package details

AuthorYing Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall
MaintainerYing Liu <[email protected]>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
summeryingliu/MIRL documentation built on May 7, 2019, 9:39 a.m.